Vicenç Acuña, Laura Castañares, J. Castellar, Joaquim Comas, Katherine Cross, D. Istenič, Fabio Masi, Robert McDonald, B. Pucher, Josep Pueyo-Ros, Adrià Riu, A. Rizzo, Massimiliano Riva, K. Tondera, Lluís Corominas
{"title":"Development of a decision-support system to select nature-based solutions for domestic wastewater treatment","authors":"Vicenç Acuña, Laura Castañares, J. Castellar, Joaquim Comas, Katherine Cross, D. Istenič, Fabio Masi, Robert McDonald, B. Pucher, Josep Pueyo-Ros, Adrià Riu, A. Rizzo, Massimiliano Riva, K. Tondera, Lluís Corominas","doi":"10.2166/bgs.2023.005","DOIUrl":null,"url":null,"abstract":"\n \n Nature-based solutions are increasingly used in domestic wastewater treatment, because of their potential to remove contaminants and pathogens from water (e.g., stormwater, river water, wastewater) as well as their provided co-benefits, such as mitigation of the heat island effect or enhanced biodiversity. The transition from traditional grey technologies towards nature-based solutions in domestic wastewater treatment might yield multiple benefits for local communities while enhancing biodiversity. Although some nature-based solutions such as treatment wetlands have been used for decades in domestic wastewater treatment, this is not the case for others such as green walls or roofs, which lack implementation guidelines and design criteria. Aiming to support implementation of nature-based solutions in domestic wastewater treatment, we have developed an online decision-support system for the pre-selection of the best nature-based solution to use in each socio-environmental context and adapted to the needs, as well as an estimate of the required area. Our decision-support system's recommendations are based on an expert knowledge-driven approach, building on two complementary expert knowledge elicitation workshops. We hope the developed online decision-support system will support the transition towards integrating nature-based solutions into urban water and wastewater treatment systems.","PeriodicalId":9337,"journal":{"name":"Blue-Green Systems","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Blue-Green Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2166/bgs.2023.005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Nature-based solutions are increasingly used in domestic wastewater treatment, because of their potential to remove contaminants and pathogens from water (e.g., stormwater, river water, wastewater) as well as their provided co-benefits, such as mitigation of the heat island effect or enhanced biodiversity. The transition from traditional grey technologies towards nature-based solutions in domestic wastewater treatment might yield multiple benefits for local communities while enhancing biodiversity. Although some nature-based solutions such as treatment wetlands have been used for decades in domestic wastewater treatment, this is not the case for others such as green walls or roofs, which lack implementation guidelines and design criteria. Aiming to support implementation of nature-based solutions in domestic wastewater treatment, we have developed an online decision-support system for the pre-selection of the best nature-based solution to use in each socio-environmental context and adapted to the needs, as well as an estimate of the required area. Our decision-support system's recommendations are based on an expert knowledge-driven approach, building on two complementary expert knowledge elicitation workshops. We hope the developed online decision-support system will support the transition towards integrating nature-based solutions into urban water and wastewater treatment systems.